Machine Learning Based PV Power Generation Forecasting in Alice Springs

IEEE Access

卷:9

Sami Azam¹  Deepika Mathur¹  Asif Karim¹  Bharanidharan Shanmugam¹  Sm Zobaed²  Khizir Mahmud³ 

[1] College of Engineering, IT and Environment, Charles Darwin University, Casuarina, NT, Australia;

[2] School of Computing and Informatics, University of Louisiana at Lafayette, Lafayette, LA, USA;

[3] School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, NSW, Australia;

关键词: Artificial intelligence;    machine learning;    power systems;    PV power forecasting;    renewable energy;    statistical regression;   

DOI  :  10.1109/ACCESS.2021.3066494

来源: DOAJ

【 摘 要 】

The generation volatility of photovoltaics (PVs) has created several control and operation challenges for grid operators. For a secure and reliable day or hour-ahead electricity dispatch, the grid operators need the visibility of their synchronous and asynchronous generators’ capacity. It helps them to manage the spinning reserve, inertia and frequency response during any contingency events. This study attempts to provide a machine learning-based PV power generation forecasting for both the short and long-term. The study has chosen Alice Springs, one of the geographically solar energy-rich areas in Australia, and considered various environmental parameters. Different machine learning algorithms, including Linear Regression, Polynomial Regression, Decision Tree Regression, Support Vector Regression, Random Forest Regression, Long Short-Term Memory, and Multilayer Perceptron Regression, are considered in the study. Various comparative performance analysis is conducted for both normal and uncertain cases and found that Random Forest Regression performed better for our dataset. The impact of data normalization on forecasting performance is also analyzed using multiple performance metrics. The study may help the grid operators to choose an appropriate PV power forecasting algorithm and plan the time-ahead generation volatility.

【 授权许可】

Unknown